In this work, we use state-of-the-art autoregressive generative model, PixelCNN++ [1]}. Here, the context \(\mathbf {x}_{<i}\)  for the conditional distribution of each of the pixels is modelled using a deep convolutional neural network with residual connections. The convolution kernels are masked appropriately to ensure that the context of a pixel does not depend on the pixels after it. The conditional distribution is then modelled as a mixture of logistic distributions, where the parameters of the distribution depend on the context. This model is then learned on RGB images using maximum likelihood training.
